Add `LogitNormalVariable` as pre-specified RV

A logit normal prior is a useful distribution. Currently, it is specified via low-level classes DistributionalVariable and TransformedVariable, e.g.:

iedr_rv = TransformedVariable(
    name="iedr",
    base_rv=DistributionalVariable(
        name="logit_iedr",
        distribution=dist.Normal(
            transformation.SigmoidTransform().inv(0.004),
            0.3,
        ),
    ),
    transforms=transformation.SigmoidTransform(),
)

Instead, it should be possible to specify:

iedr_rv = LogitNormalVariable(name="iedr",  base_name="logit_iedr",  median=0.004,  scale=0.3)

Add class LogitNormalVariable to pyrenew.randomvaraible to make this a first-class PyRenew component:

from pyrenew.randomvariable import DistributionalVariable, TransformedVariable
  import pyrenew.transformation as transformation
  import numpyro.distributions as dist

  def LogitNormalVariable(
      name: str,
      median: float,
      scale: float,
      base_name: str | None = None,
  ) -> TransformedVariable:
      return TransformedVariable(
          name=name,
          base_rv=DistributionalVariable(
              name=base_name or f"logit_{name}",
              distribution=dist.Normal(
                  transformation.SigmoidTransform().inv(median),
                  scale,
              ),
          ),
          transforms=transformation.SigmoidTransform(),
      )

Tests should verify:

• returned object is a TransformedVariable,
• base RV uses dist.Normal(logit(median), scale),
• samples lie in (0, 1),
• names are recorded as expected.

Start in the pyrenew.randomvariable module by reading DistributionalVariable and TransformedVariable, then inspect transformation.SigmoidTransform and the existing tests for these components. Add the LogitNormalVariable entry point and tests covering its return type, Normal parameters, sample bounds, and recorded names.